Optimization of Parameters in the Induction Hardening Process of a Motorcycle Engine Shaft
This study examines the optimization of processing parameters in the electromagnetic induction hardening of SAE 1045 steel shafts used in automotive applications. Induction hardening provides high energy efficiency and rapid processing but can introduce variability in metallurgical and geometric characteristics—particularly surface hardness, effective case depth, and hardened region length—potentially affecting performance in wear‑critical components.
The objective was to identify and optimize key induction parameters contributing to variability observed in production. A full factorial Design of Experiments (DOE) with two levels and four factors (16 runs) was implemented. Operational parameters of the induction system, including hardening power and coil feed speed, were selected as controllable factors, while polymer quenchant condition was treated as an uncontrollable variable.
Hardened length data were collected and analyzed through mean and standard deviation calculations, along with model fitting. The resulting predictive model quantified each factor’s influence, with a Pareto analysis revealing hardening power as the most significant contributor to variability. Based on the optimized model, an ideal operating condition of approximately 63.6 kW was determined. This setting yielded an average hardened length of 6.5 mm, a reduced standard deviation of 0.1 mm, a minimum surface hardness of 54 HRC, and an effective case depth of 1.0 mm, all meeting technical specifications.
Metallographic evaluation confirmed the presence of tempered martensite in the hardened zone, and crack detection tests showed no structural discontinuities. Importantly, the optimized condition also reduced process time by 5 seconds, reducing per‑piece induction hardening cost by 17% and further enhancing manufacturing efficiency.
The results demonstrate that systematic application of DOE is an effective approach for reducing process variability, enhancing robustness, and improving the reliability of induction‑hardened automotive components in large‑scale manufacturing.